Custom Machine Learning Development & MLOps
Custom Machine Learning Development That Ships Models to Production
Custom machine learning development that turns your data into predictions your business can act on. Predictive analytics, recommendation systems, classification and regression, forecasting, anomaly and fraud detection, computer vision, and deep learning, plus full MLOps, from data pipelines and model training to deployment, monitoring, and retraining on TensorFlow, PyTorch, scikit-learn, and cloud ML. Built by senior ML engineers with 130+ production deployments.
Built to Reach Production
- Production-Grade Accuracy
- Sub-50ms Inference
- Continuous Drift Monitoring
Triple ML Guarantee:
- Production-Grade Accuracy
- Sub-50ms Inference
- Continuous Drift Monitoring
ML Apps Shipped
0
+
Clutch Rating
0
★
Models in Production
0
+
Our ML Capabilities
Predictive Analytics
30+ Models
Recommendation Systems
25+ Engines
Computer Vision
20+ Systems
Deep Learning
40+ Networks
Forecasting
30+ Models
Fraud & Anomaly
15+ Detectors
MLOps Pipelines
60+ Pipelines
NLP & Classification
35+ Models
Model Deployment
90+ Endpoints
ML Apps Shipped
130+
Avg. P95 Inference
42ms
✓ SLA
Repeat Clients
81%
Trusted By Startups
What Sets a True Machine Learning Development Company Apart
A genuine machine learning development company does far more than train models in a Jupyter notebook. It architects end-to-end intelligent systems: your raw data flows into production pipelines, machine learning models extract predictive signals, those models deploy behind low-latency inference endpoints, and automated monitoring keeps them accurate as the real world shifts. Our ML development spans classical algorithms (XGBoost, Random Forest, SVM), deep learning (CNNs, RNNs, Transformers, GNNs), and forecasting, orchestrated on TensorFlow, PyTorch, scikit-learn, and cloud ML platforms like AWS SageMaker, Vertex AI, and Azure ML, and glued together by MLOps toolchains that turn experimental notebooks into systems your business can bet on.
Gartner and VentureBeat consistently report that 80-87% of ML projects never make it to production. They die in three predictable ways: brilliant notebooks no one can deploy, deployed models whose accuracy decays silently as data drifts, and cloud bills that 10x the moment a feature gets traction. The difference between ML that ships ROI and ML that ships technical debt is engineering discipline, MLOps, drift monitoring, model registries, feature stores, and CI/CD. Choosing the right machine learning development partner from day one is the highest-leverage decision in your AI roadmap.
Our Full ML Development Range
End-to-End ML Pipelines : Data ingestion, feature engineering, training, evaluation, registry, deployment, and monitoring, orchestrated on Airflow, Prefect, Dagster, Kubeflow, or SageMaker Pipelines, fully reproducible and CI/CD-gated.
Custom Model Development : Supervised classification and regression, unsupervised clustering, deep learning (CNN, RNN, Transformer, GAN, GNN), and time-series forecasting, architected for your problem and your data, not the latest hype paper.
Feature Stores & Data Pipelines : Centralized feature management with Feast, Tecton, SageMaker Feature Store, or Vertex Feature Store, with consistent features across training and serving and point-in-time correctness baked in.
Model Deployment & Serving: Real-time inference (Triton, TorchServe, TF Serving, BentoML, Seldon), batch transforms, serverless endpoints, and edge deployment, with sub-50ms P95 latency where it matters.
Drift Monitoring & Continuous Retraining: Data drift, concept drift, and feature-distribution monitoring with automated retraining triggers, the difference between models that compound value and models that quietly decay.
Explainability, Bias & Governance: SHAP, LIME, integrated gradients, fairness metrics across demographics, audit trails, and model cards, required in regulated industries and just smart engineering everywhere else.
Why MLOps Beats "Hire a Data Scientist and Hope"
- Production-First, Not Notebook-First: We architect for deployment from sprint one, versioned data, model registry, CI/CD, and serving, so your model reaches real traffic instead of dying on a laptop.
- MLOps Built In: Every model ships with drift monitoring, automated retraining, and observability for accuracy, latency, and feature distributions. No 'ship and forget.'
- Accuracy Tied to Business Outcomes: We optimize for the metric that moves revenue, churn saved, fraud caught, forecast error reduced, not just offline scores on a hold-out set.
- Multi-Cloud ML Fluency: Deep expertise across AWS SageMaker, Google Vertex AI, Azure ML, and Databricks, so we recommend the stack that fits your data, not the one we prefer.
- Cost-Optimized Inference: Distillation, quantization, and right-sized serving cut GPU and cloud spend 50-70% while holding sub-50ms P95 latency.
- Compliance & Governance Out of the Box: HIPAA, SR 11-7 model risk management, GDPR, and SOC 2-aligned ML with full model documentation and audit trails from day one.
How to Engage Our ML Team
Every engagement starts with a free ML discovery call. No slide deck and no sales script. You bring the business problem and whatever data you have, we audit your pipeline, benchmark a baseline on your real data, and map the path from prototype to production, and you leave with a clearer picture whether you choose to work with us or not.
We are selective about new ML engagements. We cap our active client count to protect the senior-engineer-to-project ratio our accuracy bar requires. If we say yes to your project, it is because we are confident we can ship a model you can bet the product on.
Why Data Teams Choose Us
130+
ML Apps Shipped
30+
Models In Production
42ms
Avg. P95 Inference
4.9/5
Client Rating
Ready to ship ML that actually drives business outcomes?
ML Use Cases
Machine Learning Development for Every Production Use Case
From predictive models to real-time fraud detection to computer vision, our ML development covers every production use case, end-to-end.
Predictive Analytics
Churn, risk, demand, LTV scoring
5 MODELS
Recommendation Systems
Personalization, ranking, cross-sell
4 ENGINES
Forecasting & Time Series
Demand, revenue, capacity, inventory
5 MODELS
Fraud & Anomaly Detection
Real-time scoring, drift-aware
4 CAPABILITIES
Computer Vision
Detection, OCR, segmentation, liveness
4 SYSTEMS
Classification & Regression
Scoring, tagging, pricing, NLP
5 MODELS
Predictive Maintenance
IoT sensors, failure prediction, RUL
3 MODELS
MLOps & Pipelines
CI/CD, registry, retraining, serving
6 CAPABILITIES
Deep Learning
CNN, RNN, Transformer, GNN, GAN
5 ARCHITECTURES
Data Science & Analytics
Feature stores, ETL, BI, dashboards
4 CAPABILITIES
Not sure which ML architecture fits your business problem? Let's map it together.
Common Challenges
Is Your ML Initiative Headed for the 87% That Never Ship?
These pain points signal your machine learning project is at risk of becoming an expensive science project instead of a production asset.
Notebooks That Never Deploy
01
Impressive offline accuracy on a hold-out set, and zero production deployments. The model never sees real traffic, and the pipeline lives undocumented in someone's personal repo.
Silent Model Decay & Drift
02
The model shipped, then data drifted and accuracy decayed silently for months before business KPIs flagged it. No drift monitoring, no retraining, no alarm.
Data Science Talent
Gaps
03
A solo data scientist can train a model, but production ML needs feature stores, serving, CI/CD, and MLOps. Hiring that full team takes 18 months you don't have.
Runaway GPU & Cloud Bills
04
Naive instance choices and unoptimized inference 10x your cloud bill the moment a feature gets traction. Nobody scoped for cost at scale.
Slow ROI & Long Time-to-Value
05
Six months in and there's still no model in production. Discovery loops and endless experimentation keep pushing launch to next quarter.
No MLOps, No Monitoring, No Trail
06
Past work shipped black-box models, no versioning, no registry, no explainability. Every change is a multi-week archaeology dig and every audit is a scramble.
Hitting any of these walls? Let's engineer ML you can actually ship.
Our Services in Depth
6 Core Machine Learning Development Services
From data pipelines to custom models to production MLOps, each ML service is a senior team you can run in parallel. Every line feeds every other.
Data Pipelines & Feature Stores
01
Production data ingestion, feature engineering, and centralized feature stores (Feast, Tecton, SageMaker/Vertex Feature Store) with point-in-time correctness, so training and serving stay consistent and reproducible.
Custom Model Development & Training
02
Classification, regression, clustering, forecasting, and deep learning (CNN, RNN, Transformer, GNN) on TensorFlow, PyTorch, and scikit-learn, with experiment tracking, hyperparameter tuning, and rigorous evaluation.
Deep Learning & Computer Vision
03
Image detection, segmentation, OCR, liveness, and video analytics, plus NLP and recommendation models, architected and trained for accuracy and real-time inference on your data.
Forecasting & Predictive Analytics
04
Demand forecasting, churn and risk prediction, LTV scoring, personalization, and anomaly and fraud detection, turning your historical data into predictions your team can act on.
Deployment, Serving & MLOps
05
Real-time and batch inference (Triton, TorchServe, BentoML), CI/CD for models, model registry, canary and A/B rollouts, and sub-50ms P95 serving on AWS, GCP, or Azure.
Drift Monitoring & Retraining
06
Data, concept, and prediction-drift monitoring (Evidently, Arize, WhyLabs) with automated retraining triggers, bias tracking, and explainability, so models compound value instead of decaying.
Need to combine multiple ML services into one engagement?
Why Partner with Us?
The Business Value of a Specialist Machine Learning Development Partner
What you get when one senior ML team owns the whole lifecycle, from data to deployed models to monitoring, not just one slice of it.
Models That Reach Production
01
87% of ML projects never ship. We architect for deployment from day one, so your model reaches real traffic instead of stalling in a notebook.
Faster Notebook-to-Production
02
Senior ML engineers who have shipped at scale compress the path from prototype to production without cutting corners on evaluation or MLOps.
Accuracy Tied to ROI
03
We optimize for the metric that moves your business, fraud caught, churn saved, forecast error reduced, and hold ourselves to it, not to offline scores alone.
Compliance Out of the Box
04
HIPAA, SR 11-7 model risk management, GDPR, and SOC 2-aligned ML with full model documentation, validation, and audit trails, ready for review on day one.
Sub-50ms Inference at Scale
05
Distillation, quantization, and right-sized serving deliver sub-50ms P95 latency while cutting GPU and cloud spend 50-70%.
81% Repeat Client Rate
06
Most clients come back for a second engagement, because the models hold their accuracy and keep driving ROI after launch.
Ready to ship ML that drives measurable ROI?
Our Process
From Business Problem to Production ML in 6 Proven Steps
A battle-tested ML methodology applied across predictive, vision, NLP, and forecasting engagements alike.
Discovery
Problem framing & data audit
Data Engineering
Pipelines & feature store
Modeling
Training, tuning & eval
Deployment
Serving, CI/CD, A/B
Monitoring
Drift, bias, latency
Iteration
Retraining & growth
Want to see how this process maps to your ML project?
Technology Stack
Our Machine Learning Technology Stack
End-to-end expertise across every major ML framework, cloud platform, and MLOps tool that matters in production.
Web & Backend
Next.js / React
Node.js / Vue
Python / Django
.NET / Java
TypeScript
Mobile & Cross-Platform
Swift / iOS
Kotlin / Android
React Native
Flutter
Ionic / HarmonyOS
AI / ML / Data
OpenAI / Claude
Gemini / Qwen
PyTorch / TensorFlow
Hugging Face
SageMaker / Vertex AI
Ecommerce & CMS
Shopify / Plus
BigCommerce
WooCommerce
Magento / OpenCart
Webflow / Framer
Cloud & DevOps
AWS / GCP / Azure
Docker / K8s
Terraform / IaC
GitHub Actions / CI
Datadog / Grafana
Technology Stack
Our Machine Learning Technology Stack
End-to-end expertise across every major ML framework, cloud platform, and MLOps tool that matters in production.
Web & Backend
Next.js / React
Node.js / Vue
Python / Django
.NET / Java
TypeScript
Mobile & Cross-Platform
Swift / iOS
Kotlin / Android
React Native
Flutter
Ionic / HarmonyOS
AI / ML / Data
OpenAI / Claude
Gemini / Qwen
PyTorch / TensorFlow
Hugging Face
SageMaker / Vertex AI
Ecommerce & CMS
Shopify / Plus
BigCommerce
WooCommerce
Magento / OpenCart
Webflow / Framer
Cloud & DevOps
AWS / GCP / Azure
Docker / K8s
Terraform / IaC
GitHub Actions / CI
Datadog / Grafana
Industries We Serve
Machine Learning Across Every High-Stakes Vertical
Deep domain knowledge across every industry where predictive AI and intelligent automation are the competitive moat.
Fintech & Banking
Payments, KYC, regulatory tech
Healthcare & HealthTech
HIPAA, telehealth, clinical SaaS
Retail & E-Commerce
DTC, B2B, marketplace, headless
EdTech & Learning
LMS, course platforms, proctoring
Manufacturing & Industrial
IoT, predictive maintenance, MES
Logistics & Supply Chain
Routing, fleet, warehouse, B2B
Legal & LegalTech
Document AI, contract analysis
Media & Entertainment
Streaming, content AI, audience
We understand your vertical. Let's build ML your team can trust.
Why Choose Us?
How We Compare To Alternatives
An honest look at your machine learning development options.
| Capability | DIY / Notebooks | Solo Data Scientist | Generic AI Agency | Stallyons Technologies |
|---|---|---|---|---|
| End-to-End ML Pipelines | ✕ Manual Scripts | Notebook-Only | Basic | Production CI/CD |
| MLOps & Model Registry | ✕ None | ✕ Rare | Premium | MLflow / W&B |
| Sub-50ms P95 Inference | ✕ No Optimization | ✕ Rare | Sometimes | Distilled + Quantized |
| Drift & Bias Monitoring | ✕ | ✕ Forgotten | Extra Cost | Continuous |
| Multi-Cloud (AWS / GCP / Azure) | ✕ | One Cloud | Limited | All Three |
| Explainability (SHAP / LIME) | ✕ | Rare | Specialty | Every Prediction |
| Compliance (HIPAA / SR 11-7) | ✕ | ✕ Risky | Specialty | Compliant by Design |
| Cost Optimization | ✕ Naive Instances | ✕ | Sometimes | 50-70% Savings |
See the ML engineering difference for yourself
Complete Engagement
Everything You Get with an ML Development Partnership
From Business Problem to Production ML & MLOps, All Under One Roof
Here's everything included when you partner with Stallyons as your ML development agency:
Complete ML Development Package: No Hidden Costs.
Every engagement includes all 8 components above. Get a custom quote tailored to your use case, data volume, deployment target, and compliance posture.
🔒 No obligation. We'll deliver a detailed proposal within 48 hours.
Plus, Get These Free Bonuses
Free ML Pipeline Audit
A 30-point review of your data, current models, MLOps maturity, and drift exposure. Yours free whether you sign or not.
Included Free
ML Roadmap & Estimate
A phased delivery plan from prototype to production, with milestones, data dependencies, and transparent effort estimates.
Included Free
Baseline Model PoC Sprint
For qualifying engagements, a 1-week proof-of-concept sprint that benchmarks a baseline model on your real data before you commit.
Included Free
Risk-Free Partnership
Our Triple ML Guarantee
We stand behind every ML engagement with commitments that protect your investment
01
Production-Grade Accuracy
We benchmark on your real data and hold to the accuracy targets we set together. If a model misses the numbers, we keep iterating until it hits them, at no extra cost.
02
Sub-50ms Inference
We optimize every production model for latency, benchmarking P50, P95, and P99 under realistic load, with distillation and quantization to hold sub-50ms where it matters.
03
Continuous Drift Monitoring
Every model ships with drift, bias, and performance monitoring plus automated retraining. Models decay; we make sure yours are watched and refreshed, not shipped and forgotten.
Build with zero risk, backed by our Triple ML Guarantee
Track Record
Engagements That Ship, Scale, and Compound
500+
Projects Delivered
29+
Service Categories
81%
Repeat Client Rate
4.9 ★
Clutch Rating
"We came to Stallyons after burning two years and four vendors on a multi-platform launch that kept slipping. They scoped it end-to-end — web app, iOS, Android, an AI summarization layer, and a Shopify integration — and shipped it in 22 weeks. One team, one budget, one quality bar. We've handed them three more engagements since."
Mark Sawyer
CEO/Founder
PlatinumLED
"Stallyons rebuilt our customer-facing portal, integrated three legacy systems, shipped an AI document analysis pipeline, and brought our compliance posture to SOC 2 — all under one engagement. The senior engineers on the team have shipped at companies five times our size. It's the best vendor decision we've made in a decade."
Mark Sawyer
CEO/Founder
PlatinumLED
FAQ
Frequently Asked Questions
ML development costs vary based on scope, data volume, model complexity, deployment target, MLOps maturity, and compliance posture. A single-model PoC is a very different investment than a multi-model production platform with MLOps, feature store, and drift monitoring. We provide detailed, transparent estimates after a free discovery call. No slide-deck-driven sticker shock.
It depends on your existing cloud, data warehouse, and team skills. SageMaker is strongest for AWS-native shops with deep integration to Redshift, S3, and Bedrock. Vertex AI shines for BigQuery-heavy teams and Google Cloud integration. Azure ML is the enterprise default for Microsoft-centric organizations and HIPAA-aligned deployments. Databricks wins when Spark is already central. We benchmark all four during discovery and recommend honestly based on your stack, not our preferences.
For generic use cases at low volume, pre-built APIs (AWS Rekognition, Vertex AutoML, Azure Cognitive Services) often ship value fastest. For domain-specific problems, high-volume production, regulatory constraints, or use cases where accuracy directly drives revenue, custom models almost always win on accuracy AND cost. We benchmark both during discovery and recommend honestly. Sometimes the answer is “stay on AutoML.” Sometimes it’s “build a custom XGBoost or transformer.”
Versioned datasets and features (DVC, Feast, Tecton), experiment tracking (MLflow, Weights & Biases, Neptune), model registry with promotion gates, CI/CD for models via GitHub Actions or GitLab CI, automated retraining triggered by drift, A/B and canary deployments, and full observability (latency, accuracy, feature distributions). Built on MLflow, Kubeflow, Airflow, or SageMaker Pipelines depending on your stack.
Model distillation (training a smaller student from a larger teacher), quantization (FP32 → INT8 / INT4), ONNX Runtime or TensorRT optimization, GPU batching via Triton, and aggressive caching where appropriate. We benchmark P50, P95, and P99 latency under realistic load, not just averages on a dev machine. For edge use cases we add TensorFlow Lite, CoreML, OpenVINO, or hardware-specific optimization.
Yes. We ship HIPAA-aligned ML for clinical, telemedicine, and payer use cases (BAAs, AWS HIPAA-eligible services, Azure with BAA, on-premise where required). For financial services we build to SR 11-7 model risk management, with full model documentation, validation, monitoring, and audit trails. PCI DSS, GDPR, CCPA, and SOC 2 postures are standard practice, we document every decision for your compliance teams.
We instrument every production model with data drift, concept drift, feature distribution monitoring, prediction monitoring, and bias monitoring, via Evidently AI, WhyLabs, Arize, or custom stacks. Drift triggers automated retraining pipelines that produce challenger models, A/B them against the champion, and promote winners through the model registry, with rollback ready if anything regresses.
Yes. We offer retainer-based MLOps covering drift monitoring, automated retraining, model performance tracking, infrastructure cost optimization, new model rollouts, A/B testing infrastructure, incident response, and continuous improvement. ML models decay, your build needs continuous evaluation, not “ship and forget.”
Still have questions? Let's talk.
Schedule an appointment with us today!
Ready to Ship ML That Reaches Production?
Get a free ML consultation. We'll audit your current pipeline, benchmark a baseline on your real data, and map a clear roadmap from prototype to production, at no cost.
